Fuzzy Adaptive Particle Swarm Optimization for Optimal Operation of Cascaded Hydropower Station

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Fuzzy Adaptive Particle Swarm Optimization for Optimal Operation of Cascaded Hydropower Station

更新时间：2025-05-08

- Fuzzy Adaptive Particle Swarm Optimization for Optimal Operation of Cascaded Hydropower Station
- Vol. 43, Issue 6, Pages: 93-98(2009)
- 作者机构：
  
  西安交通大学电气工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TV697.1
- Online First：10 June 2009，
  
  Published：2009
- 稿件说明：
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Fuzzy Adaptive Particle Swarm Optimization for Optimal Operation of Cascaded Hydropower Station[J]. 2009, 43(6): 93-98.
DOI：

Fuzzy Adaptive Particle Swarm Optimization for Optimal Operation of Cascaded Hydropower Station[J]. 2009, 43(6): 93-98. DOI：

摘要

针对粒子群算法容易早熟和易于陷入局部极值的缺点

提出一种梯级水电站优化调度的模糊自适应粒子群算法.在该算法中将惯性权值表示为粒子群进化速度因子和群体适应度方差的模糊函数

在每次迭代过程中动态改变惯性权值

以适应非线性优化搜索过程.针对违反约束的粒子

设计了一种动态空间调整策略来修复约束要求.为了验证算法的性能

用2个测试函数和拥有4个水电站的系统进行了测试

在求解精度和速度上与标准粒子群算法和改进惯性权值线性递减粒子群算法进行了对比

结果表明模糊自适应粒子群算法收敛速度快、精度高.

Abstract

A fuzzy adaptive particle swarm optimization(FAPSO)for optimal operation of cascaded hydropower station is presented to solve the shortcomings of premature and easily getting into local optimum in standard particle swarm optimization(PSO). The fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and fitness variance of the swarm. In each iteration process

the inertia weight is dynamically changed following the fuzzy rules to adapt to the nonlinear optimization process. In order to deal with the constraints

a dynamic search-space adjustment strategy is devised to accelerate the optimization process. The performance of FAPSO is demonstrated on two testing functions and a cascaded hydropower station with 4 reservoirs

and comparison is drawn among PSO

LDWPSO(linearly decreasing weight particle swarm optimization)and FAPSO in terms of the solution quality and computational efficiency. The simulation shows that FAPSO has higher convergence rate and accuracy in global search.

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references

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